{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from config import *\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020年5月\n"
     ]
    }
   ],
   "source": [
    "print(f'{year}年{month}月')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('../../py')\n",
    "import db\n",
    "import weighted\n",
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_original=pd.read_sql(sql=f\"select * from _{year}{month:02} where monthly_salary>0 and monthly_salary<80000\", con=db.get_conn())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103220, 114)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_original.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data_original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'company_title'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2645\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2646\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2647\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'company_title'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-7-1f95411a0cb6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'published_on_weekend'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'title'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mdel\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'company_title'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'company_description'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'job_description'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__delitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3756\u001b[0m             \u001b[1;31m# there was no match, this call should raise the appropriate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3757\u001b[0m             \u001b[1;31m# exception:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3758\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3759\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3760\u001b[0m         \u001b[1;31m# delete from the caches\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mdelete\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m   1000\u001b[0m         \u001b[0mDelete\u001b[0m \u001b[0mselected\u001b[0m \u001b[0mitem\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mitems\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mnon\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mplace\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1001\u001b[0m         \"\"\"\n\u001b[1;32m-> 1002\u001b[1;33m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1003\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1004\u001b[0m         \u001b[0mis_deleted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbool_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2646\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2647\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2648\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2650\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'company_title'"
     ]
    }
   ],
   "source": [
    "del data['publish_date']\n",
    "del data['published_on_weekend']\n",
    "del data['title']\n",
    "del data['company_title']\n",
    "del data['company_description']\n",
    "del data['job_description']\n",
    "del data['job_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sub_stats_by_prefix(data, prefix):\n",
    "    \n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    salary_mean=[]\n",
    "    salary_median=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    count=[]\n",
    "    \n",
    "    features_out=[]\n",
    "    for feature in features:\n",
    "        #print(feature)\n",
    "        idata=data[data[feature]==1]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(weighted.weighted_mean(values, weights))\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        features_out.append(feature)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data['rank']=range(0,len(features_out))\n",
    "    sub_data[prefix]=[f.replace(prefix,'') for f in features_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "    sub_data['rank']=range(1,len(features_out)+1)\n",
    "    #sub_data=sub_data.reset_index()\n",
    "    return sub_data\n",
    "\n",
    "def apply_style(sub_data):\n",
    "    return sub_data.style.hide_index().format(\n",
    "        {\"percentage\":\"{:.2%}\",\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"salary_95_min\":\"{:.0f}\",\"salary_95_max\":\"{:.0f}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'pl_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_bf776240_8b68_11ea_87db_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >rank</th>        <th class=\"col_heading level0 col1\" >pl_</th>        <th class=\"col_heading level0 col2\" >salary_mean</th>        <th class=\"col_heading level0 col3\" >salary_median</th>        <th class=\"col_heading level0 col4\" >salary_95_min</th>        <th class=\"col_heading level0 col5\" >salary_95_max</th>        <th class=\"col_heading level0 col6\" >head_count</th>        <th class=\"col_heading level0 col7\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow0_col1\" class=\"data row0 col1\" >scala</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow0_col2\" class=\"data row0 col2\" >20165</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow0_col3\" class=\"data row0 col3\" >18000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow0_col4\" class=\"data row0 col4\" >7000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow0_col5\" class=\"data row0 col5\" >45000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow0_col6\" class=\"data row0 col6\" >3309</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow0_col7\" class=\"data row0 col7\" >0.71%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow1_col1\" class=\"data row1 col1\" >rust</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow1_col2\" class=\"data row1 col2\" >19364</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow1_col3\" class=\"data row1 col3\" >17500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow1_col4\" class=\"data row1 col4\" >5398</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow1_col5\" class=\"data row1 col5\" >43687</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow1_col6\" class=\"data row1 col6\" >483</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow1_col7\" class=\"data row1 col7\" >0.10%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow2_col1\" class=\"data row2 col1\" >python</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow2_col2\" class=\"data row2 col2\" >18622</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow2_col3\" class=\"data row2 col3\" >17500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow2_col4\" class=\"data row2 col4\" >6500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow2_col5\" class=\"data row2 col5\" >45000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow2_col6\" class=\"data row2 col6\" >36531</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow2_col7\" class=\"data row2 col7\" >7.81%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow3_col1\" class=\"data row3 col1\" >julia</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow3_col2\" class=\"data row3 col2\" >18455</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow3_col3\" class=\"data row3 col3\" >20214</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow3_col4\" class=\"data row3 col4\" >12500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow3_col5\" class=\"data row3 col5\" >25000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow3_col6\" class=\"data row3 col6\" >11</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow3_col7\" class=\"data row3 col7\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow4_col1\" class=\"data row4 col1\" >go</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow4_col2\" class=\"data row4 col2\" >18379</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow4_col3\" class=\"data row4 col3\" >17500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow4_col4\" class=\"data row4 col4\" >7000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow4_col5\" class=\"data row4 col5\" >42500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow4_col6\" class=\"data row4 col6\" >32180</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow4_col7\" class=\"data row4 col7\" >6.88%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow5_col1\" class=\"data row5 col1\" >lua</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow5_col2\" class=\"data row5 col2\" >18349</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow5_col3\" class=\"data row5 col3\" >17357</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow5_col4\" class=\"data row5 col4\" >6000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow5_col5\" class=\"data row5 col5\" >40000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow5_col6\" class=\"data row5 col6\" >4328</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow5_col7\" class=\"data row5 col7\" >0.93%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow6_col0\" class=\"data row6 col0\" >7</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow6_col1\" class=\"data row6 col1\" >matlab</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow6_col2\" class=\"data row6 col2\" >18295</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow6_col3\" class=\"data row6 col3\" >17500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow6_col4\" class=\"data row6 col4\" >7000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow6_col5\" class=\"data row6 col5\" >37500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow6_col6\" class=\"data row6 col6\" >5800</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow6_col7\" class=\"data row6 col7\" >1.24%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow7_col0\" class=\"data row7 col0\" >8</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow7_col1\" class=\"data row7 col1\" >ruby</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow7_col2\" class=\"data row7 col2\" >17851</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow7_col3\" class=\"data row7 col3\" >15800</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow7_col4\" class=\"data row7 col4\" >5250</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow7_col5\" class=\"data row7 col5\" >45000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow7_col6\" class=\"data row7 col6\" >1557</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow7_col7\" class=\"data row7 col7\" >0.33%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow8_col0\" class=\"data row8 col0\" >9</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow8_col1\" class=\"data row8 col1\" >r</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow8_col2\" class=\"data row8 col2\" >17513</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow8_col3\" class=\"data row8 col3\" >16000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow8_col4\" class=\"data row8 col4\" >7000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow8_col5\" class=\"data row8 col5\" >37500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow8_col6\" class=\"data row8 col6\" >3760</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow8_col7\" class=\"data row8 col7\" >0.80%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow9_col0\" class=\"data row9 col0\" >10</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow9_col1\" class=\"data row9 col1\" >swift</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow9_col2\" class=\"data row9 col2\" >17167</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow9_col3\" class=\"data row9 col3\" >15000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow9_col4\" class=\"data row9 col4\" >6969</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow9_col5\" class=\"data row9 col5\" >37500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow9_col6\" class=\"data row9 col6\" >2805</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow9_col7\" class=\"data row9 col7\" >0.60%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow10_col0\" class=\"data row10 col0\" >11</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow10_col1\" class=\"data row10 col1\" >haskell</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow10_col2\" class=\"data row10 col2\" >17157</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow10_col3\" class=\"data row10 col3\" >15000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow10_col4\" class=\"data row10 col4\" >11500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow10_col5\" class=\"data row10 col5\" >32250</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow10_col6\" class=\"data row10 col6\" >35</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow10_col7\" class=\"data row10 col7\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow11_col0\" class=\"data row11 col0\" >12</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow11_col1\" class=\"data row11 col1\" >perl</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow11_col2\" class=\"data row11 col2\" >17109</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow11_col3\" class=\"data row11 col3\" >15000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow11_col4\" class=\"data row11 col4\" >5250</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow11_col5\" class=\"data row11 col5\" >45000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow11_col6\" class=\"data row11 col6\" >2794</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow11_col7\" class=\"data row11 col7\" >0.60%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow12_col0\" class=\"data row12 col0\" >13</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow12_col1\" class=\"data row12 col1\" >kotlin</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow12_col2\" class=\"data row12 col2\" >16433</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow12_col3\" class=\"data row12 col3\" >15000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow12_col4\" class=\"data row12 col4\" >7000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow12_col5\" class=\"data row12 col5\" >31000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow12_col6\" class=\"data row12 col6\" >1140</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow12_col7\" class=\"data row12 col7\" >0.24%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow13_col0\" class=\"data row13 col0\" >14</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow13_col1\" class=\"data row13 col1\" >cpp</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow13_col2\" class=\"data row13 col2\" >15441</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow13_col3\" class=\"data row13 col3\" >13000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow13_col4\" class=\"data row13 col4\" >5500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow13_col5\" class=\"data row13 col5\" >37500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow13_col6\" class=\"data row13 col6\" >113032</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow13_col7\" class=\"data row13 col7\" >24.16%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow14_col0\" class=\"data row14 col0\" >15</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow14_col1\" class=\"data row14 col1\" >objective_c</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow14_col2\" class=\"data row14 col2\" >14929</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow14_col3\" class=\"data row14 col3\" >13000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow14_col4\" class=\"data row14 col4\" >6062</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow14_col5\" class=\"data row14 col5\" >31944</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow14_col6\" class=\"data row14 col6\" >250</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow14_col7\" class=\"data row14 col7\" >0.05%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow15_col0\" class=\"data row15 col0\" >16</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow15_col1\" class=\"data row15 col1\" >typescript</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow15_col2\" class=\"data row15 col2\" >14824</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow15_col3\" class=\"data row15 col3\" >12500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow15_col4\" class=\"data row15 col4\" >7000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow15_col5\" class=\"data row15 col5\" >32500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow15_col6\" class=\"data row15 col6\" >3612</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow15_col7\" class=\"data row15 col7\" >0.77%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow16_col0\" class=\"data row16 col0\" >17</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow16_col1\" class=\"data row16 col1\" >php</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow16_col2\" class=\"data row16 col2\" >14701</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow16_col3\" class=\"data row16 col3\" >12500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow16_col4\" class=\"data row16 col4\" >5250</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow16_col5\" class=\"data row16 col5\" >40000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow16_col6\" class=\"data row16 col6\" >16199</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow16_col7\" class=\"data row16 col7\" >3.46%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow17_col0\" class=\"data row17 col0\" >18</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow17_col1\" class=\"data row17 col1\" >java</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow17_col2\" class=\"data row17 col2\" >14545</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow17_col3\" class=\"data row17 col3\" >12500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow17_col4\" class=\"data row17 col4\" >5250</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow17_col5\" class=\"data row17 col5\" >35000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow17_col6\" class=\"data row17 col6\" >127049</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow17_col7\" class=\"data row17 col7\" >27.15%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow18_col0\" class=\"data row18 col0\" >19</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow18_col1\" class=\"data row18 col1\" >c_sharp</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow18_col2\" class=\"data row18 col2\" >12888</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow18_col3\" class=\"data row18 col3\" >12000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow18_col4\" class=\"data row18 col4\" >5250</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow18_col5\" class=\"data row18 col5\" >30000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow18_col6\" class=\"data row18 col6\" >43249</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow18_col7\" class=\"data row18 col7\" >9.24%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow19_col0\" class=\"data row19 col0\" >20</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow19_col1\" class=\"data row19 col1\" >visual_basic</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow19_col2\" class=\"data row19 col2\" >12729</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow19_col3\" class=\"data row19 col3\" >12500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow19_col4\" class=\"data row19 col4\" >5250</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow19_col5\" class=\"data row19 col5\" >22500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow19_col6\" class=\"data row19 col6\" >36</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow19_col7\" class=\"data row19 col7\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow20_col0\" class=\"data row20 col0\" >21</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow20_col1\" class=\"data row20 col1\" >javascript</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow20_col2\" class=\"data row20 col2\" >12452</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow20_col3\" class=\"data row20 col3\" >11666</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow20_col4\" class=\"data row20 col4\" >5250</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow20_col5\" class=\"data row20 col5\" >25000</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow20_col6\" class=\"data row20 col6\" >68127</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow20_col7\" class=\"data row20 col7\" >14.56%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow21_col0\" class=\"data row21 col0\" >22</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow21_col1\" class=\"data row21 col1\" >delphi</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow21_col2\" class=\"data row21 col2\" >11293</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow21_col3\" class=\"data row21 col3\" >10500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow21_col4\" class=\"data row21 col4\" >5250</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow21_col5\" class=\"data row21 col5\" >22500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow21_col6\" class=\"data row21 col6\" >1075</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow21_col7\" class=\"data row21 col7\" >0.23%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow22_col0\" class=\"data row22 col0\" >23</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow22_col1\" class=\"data row22 col1\" >vba</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow22_col2\" class=\"data row22 col2\" >10742</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow22_col3\" class=\"data row22 col3\" >10500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow22_col4\" class=\"data row22 col4\" >4016</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow22_col5\" class=\"data row22 col5\" >22500</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow22_col6\" class=\"data row22 col6\" >525</td>\n",
       "                        <td id=\"T_bf776240_8b68_11ea_87db_701ce71031efrow22_col7\" class=\"data row22 col7\" >0.11%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25ee2a79588>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23, 8)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pl=data_pl[['pl_','percentage']].sort_values(by='percentage', ascending=False)\n",
    "data_pl.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031ef\" ><thead>    <tr>        <th class=\"col_heading level0 col0\" >rank</th>        <th class=\"col_heading level0 col1\" >pl_</th>        <th class=\"col_heading level0 col2\" >percentage</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow0_col1\" class=\"data row0 col1\" >java</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow0_col2\" class=\"data row0 col2\" >27.15%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow1_col1\" class=\"data row1 col1\" >cpp</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow1_col2\" class=\"data row1 col2\" >24.16%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow2_col1\" class=\"data row2 col1\" >javascript</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow2_col2\" class=\"data row2 col2\" >14.56%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow3_col1\" class=\"data row3 col1\" >c_sharp</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow3_col2\" class=\"data row3 col2\" >9.24%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow4_col1\" class=\"data row4 col1\" >python</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow4_col2\" class=\"data row4 col2\" >7.81%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow5_col1\" class=\"data row5 col1\" >go</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow5_col2\" class=\"data row5 col2\" >6.88%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow6_col0\" class=\"data row6 col0\" >7</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow6_col1\" class=\"data row6 col1\" >php</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow6_col2\" class=\"data row6 col2\" >3.46%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow7_col0\" class=\"data row7 col0\" >8</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow7_col1\" class=\"data row7 col1\" >matlab</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow7_col2\" class=\"data row7 col2\" >1.24%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow8_col0\" class=\"data row8 col0\" >9</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow8_col1\" class=\"data row8 col1\" >lua</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow8_col2\" class=\"data row8 col2\" >0.93%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow9_col0\" class=\"data row9 col0\" >10</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow9_col1\" class=\"data row9 col1\" >r</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow9_col2\" class=\"data row9 col2\" >0.80%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow10_col0\" class=\"data row10 col0\" >11</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow10_col1\" class=\"data row10 col1\" >typescript</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow10_col2\" class=\"data row10 col2\" >0.77%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow11_col0\" class=\"data row11 col0\" >12</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow11_col1\" class=\"data row11 col1\" >scala</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow11_col2\" class=\"data row11 col2\" >0.71%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow12_col0\" class=\"data row12 col0\" >13</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow12_col1\" class=\"data row12 col1\" >swift</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow12_col2\" class=\"data row12 col2\" >0.60%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow13_col0\" class=\"data row13 col0\" >14</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow13_col1\" class=\"data row13 col1\" >perl</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow13_col2\" class=\"data row13 col2\" >0.60%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow14_col0\" class=\"data row14 col0\" >15</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow14_col1\" class=\"data row14 col1\" >ruby</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow14_col2\" class=\"data row14 col2\" >0.33%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow15_col0\" class=\"data row15 col0\" >16</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow15_col1\" class=\"data row15 col1\" >kotlin</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow15_col2\" class=\"data row15 col2\" >0.24%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow16_col0\" class=\"data row16 col0\" >17</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow16_col1\" class=\"data row16 col1\" >delphi</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow16_col2\" class=\"data row16 col2\" >0.23%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow17_col0\" class=\"data row17 col0\" >18</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow17_col1\" class=\"data row17 col1\" >vba</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow17_col2\" class=\"data row17 col2\" >0.11%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow18_col0\" class=\"data row18 col0\" >19</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow18_col1\" class=\"data row18 col1\" >rust</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow18_col2\" class=\"data row18 col2\" >0.10%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow19_col0\" class=\"data row19 col0\" >20</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow19_col1\" class=\"data row19 col1\" >objective_c</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow19_col2\" class=\"data row19 col2\" >0.05%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow20_col0\" class=\"data row20 col0\" >21</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow20_col1\" class=\"data row20 col1\" >visual_basic</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow20_col2\" class=\"data row20 col2\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow21_col0\" class=\"data row21 col0\" >22</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow21_col1\" class=\"data row21 col1\" >haskell</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow21_col2\" class=\"data row21 col2\" >0.01%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                                <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow22_col0\" class=\"data row22 col0\" >23</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow22_col1\" class=\"data row22 col1\" >julia</td>\n",
       "                        <td id=\"T_c1f37e02_8b68_11ea_83a7_701ce71031efrow22_col2\" class=\"data row22 col2\" >0.00%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x25ed32119c8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "data_pl['rank']=list(range(1,24))\n",
    "data_pl=data_pl[['rank','pl_','percentage']]\n",
    "#data_pl.style.format({\"percentage\":\"{:.2%}\"})\n",
    "data_pl.style.hide_index().format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_pl=data_pl.sort_values(by='percentage')\n",
    "\n",
    "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
    "labels = data_pl['pl_']\n",
    "sizes = data_pl['percentage']\n",
    "#explode = (0, 0.1, 0, 0)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize=(15, 9))\n",
    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.title(f\"Programming Languages in China, {year}{month:02}\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word Cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud\n",
    "wc=WordCloud()\n",
    "wc_dict = {}\n",
    "for index, row in data_pl.iterrows():\n",
    "    wc_dict[row['pl_']]=row['percentage']\n",
    "\n",
    "\n",
    "wc=wc.generate_from_frequencies(wc_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "wc_dict['c/c++']=0.33\n",
    "wc=wc.generate_from_frequencies(wc_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.imshow(wc, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f'{year}年{month}月')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
